Quantum-driven zero trust architecture with dynamic anomaly detection in 7G technology: A neural network approach

As cyber threats grow in complexity, modern networks face significant challenges in mitigating evolving attack patterns while maintaining scalability and computational efficiency. Quantum computing presents a promising approach to enhancing cybersecurity; however, its adoption is hindered by scalability constraints, inefficiencies in quantum data encoding, and the high computational costs associated with quantum processing. To address these limitations, we propose the Quantum Neural Network-Enhanced Zero Trust Architecture (QNN-ZTA), which integrates ZTA, Intrusion Detection Systems, and QNN to strengthen security capabilities and dynamic risk-based policy enforcement. Leveraging quantum principles such as superposition, entanglement, and variational optimization, QNN-ZTA facilitates real-time anomaly detection and adaptive policy enforcement across large-scale networks. The primary contributions of the proposed framework include 1) hybrid quantum-classical architecture for balancing computational costs and ensuring scalability and 2) dynamic quantum-enhanced anomaly scoring and adaptive risk-based policies to improve detection accuracy and responsiveness. Additionally, quantum-powered micro-segmentation dynamically isolates high-risk network regions, preventing lateral movement of attackers and strengthening overall network security. The framework incorporates a hybrid quantum-classical security architecture to balance computational efficiency, quantum-enhanced anomaly scoring to adjust risk-based policies dynamically, and quantum micro-segmentation to restrict attacker movement and isolate high-risk segments. Our evaluation demonstrates that QNN-ZTA significantly reduces false positives, improves threat detection accuracy, and enhances response times, with a case study in enterprise cybersecurity showing an 87 % improvement in cyber threat mitigation efficiency. This research establishes a scalable, adaptive, and quantum-optimized cybersecurity model, paving the way for future advancements in quantum-enhanced threat defense.